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Dongil “Dan” Cho (RS Automation, Korea)
Control Platform |
| Over the years, we have explored and utilized various control methods, including PID control, optimal control, nonlinear control, robust control, model-predictive control, and data-driven control, to name a few. However, real control is far from simple; beneath the surface, a plethora of additional methods must be integrated and finely tuned to achieve the desired performance. Integrating many control methods to work seamlessly together and tuning several tens of parameters can prove extremely challenging due to the lack of a systematic method. To address this challenge, we have developed a Control Platform for motion control systems. This Platform is based on sliding mode control theory, which incorporates multiple other methods into a unified platform and utilizes a single gain to prescribe a final performance level while maintaining asymptotic stability and robustness. Through extensive simulation experiments, laboratory tests, and prototype experiments, we have achieved exceptional performance results in real motion control systems.
Education: Other Experiences: |
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Florian Dörfler (Swiss Federal Institute of Technology, Switzerland)
Data-Driven Control Based on Behavioral Systems Theory |
| We consider the problem of optimal and constrained control for unknown systems. A novel data-enabled predictive control method is presented that computes optimal and safe control policies. Using a finite number of data samples from the unknown system, our method uses a behavioral systems theory approach to learn a non-parametric system model used to predict future trajectories. To cope with nonlinearities and stochasticities, we propose salient regularizations to our problem formulation. Using techniques from optimal transport and distributionally robust optimization, we prove that these regularization indeed robustify our method. We show that, in the case of deterministic linear time-invariant systems, our method is equivalent to the widely adopted model predictive control, but it can also outperform subsequent system identification and model-based control. We illustrate our results with nonlinear and noisy simulations and experiments from robotics, power electronics, and power systems
Florian Dörfler is an Associate Professor at the Automatic Control Laboratory at ETH Zürich. He received his Ph.D. degree in Mechanical Engineering from the University of California at Santa Barbara in 2013, and a Diplom degree in Engineering Cybernetics from the University of Stuttgart in 2008. From 2013 to 2014 he was an Assistant Professor at the University of California Los Angeles. He has been serving as the Associate Head of the ETH Zürich Department of Information Technology and Electrical Engineering from 2021 until 2022. His research interests are centered around automatic control, system theory, and optimization. His particular foci are on network systems, data-driven settings, and applications to power systems. He is a recipient of the distinguished young research awards by IFAC (Manfred Thoma Medal 2020) and EUCA (European Control Award 2020). His students were winners or finalists for Best Student Paper awards at the European Control Conference (2013, 2019), the American Control Conference (2016), the Conference on Decision and Control (2020), the PES General Meeting (2020), the PES PowerTech Conference (2017), the International Conference on Intelligent Transportation Systems (2021), and the IEEE CSS Swiss Chapter Young Author Best Journal Paper Award (2022). He is furthermore a recipient of the 2010 ACC Student Best Paper Award, the 2011 O. Hugo Schuck Best Paper Award, the 2012-2014 Automatica Best Paper Award, the 2016 IEEE Circuits and Systems Guillemin-Cauer Best Paper Award, the 2022 IEEE Transactions on Power Electronics Prize Paper Award, and the 2015 UCSB ME Best PhD award. He is currently serving on the council of the European Control Association and as a senior editor of Automatica. |
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Lei Guo (Beihang University, China)
Refined estimation and control for systems with composite disturbances: From theory to application |
| Practical systems have become increasingly complicated, and inevitably suffer from composite disturbances that are physically multi-source, mathematically heterogeneous, and topologically isomeric. In the conventional estimation and control methodologies, these composite disturbances are simplified as a lumped or “equivalent” variable to be either rejected or attenuated. To overcome this single disturbance limitation, the refined anti-disturbance estimation (RADE) and control (RADC) theoretical framework for multiple disturbances will be introduced in this talk. The refined anti-disturbance control (formerly known as composite hierarchical anti-disturbance control, CHADC), together with its duality theory–RADE, have been widely validated and applied to various precision motion control systems such as robots and aerospace systems. The refined estimation and control scheme includes disturbance modeling and estimation/learning/prediction, multiple disturbance separation, observability and controllability analysis of disturbances, disturbance absorption and utilization, as well as reconstruction optimization based on disturbance adaptive variability rather than the disturbance invariance principle (DIP). Especially, aiming at the limitation of Gaussian and independent identically distributed variables in Kalman filtering and Bayesian estimation theory, this research framework focuses on stochastic distribution control (SDC) and estimation, as well as non-Gaussian statistical information optimization for systems with composite non-Gaussian disturbances. Finally, several practical applications will be presented to the autonomous navigation and control of unmanned systems.
Professor Lei Guo was born in Qufu, China, in 1966. He received the B.S. degree and the M.S. degree from Qufu Normal University, in 1988 and 1991, respectively, and the Ph.D. degree from Southeast University, Nanjing, China, in 1997. Currently, he is a Distinguish Professor and Director of the Space Intelligent Control Research Center at Beihang University, Beijing, China. |
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Yoshito Ohta (Kyoto University, Japan)
Positive systems from the modeling viewpoint |
| Positive systems are systems whose variables are constrained to be nonnegative. Such constraints emerge when we handle material quantities and concentrations, populations, probabilities, etc. Positive systems exhibit distinctive properties in contrast with systems without nonnegative constraints when we control and analyze them or try to construct a model. In this talk, we focus on the modeling part of positive systems and elucidate the three subjects from the modeling viewpoint: the realization problem, identification methods, and the hidden Markov model (HMM) realization problem. The realization problem is a problem of finding a linear state space model given a transfer function. Unlike the realization problem of ordinary linear systems that can be solved by the Ho-Kalman algorithm, the minimal degree of realization may not be equal to the rank of the Hankel matrix because of nonnegative constraints. Though this is an old problem, it gives an insight into the HMM realization problem, which is mentioned later. Identification of positive systems seeks a system model from input-output data, considering nonnegative constraints. After reviewing several methods, we refer to the Bayes approach to finite impulse response (FIR) systems, where nonnegative constraints are handled by a prior distribution. The HMM realization problem is a problem of finding an underlying Markov process and an output mapping from the probabilities of the output process taking values in a finite alphabet. Just like the realization problem of positive systems, it is challenging to minimize the number of states in the Markov process among all the realization. We mention a reduction procedure of the realization that uses the structure of interconnection of the states in the Markov process.
Yoshito Ohta received a Doctor of Engineering Degree in Electronic Engineering in 1986 from Osaka University, Japan. In 1983, he joined the Department of Electronic Engineering at Osaka University as a Research Associate. From 1986 to 1988, he was a Visiting Scientist at the Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, USA. He became a Professor in 1999 at the Department of Computer-Controlled Mechanical Systems, Osaka University. From 2006 to 2023, he was a Professor at the Department of Applied Mathematics and Physics, Kyoto University. He is a Professor Emeritus at Kyoto University. His research interest lies in robust control, networked control, and system modeling. |
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Jan Peters (TU Darmstadt, Germany)
Inductive Biases for Robot Reinforcement Learning |
| Autonomous robots that can assist humans in situations of daily life have been a long standing vision of robotics, artificial intelligence, and cognitive sciences. A first step towards this goal is to create robots that can learn tasks triggered by environmental context or higher level instruction. However, learning techniques have yet to live up to this promise as only few methods manage to scale to high-dimensional manipulator or humanoid robots. In this talk, we investigate a general framework suitable for learning motor skills in robotics which is based on the principles behind many analytical robotics approaches. To accomplish robot reinforcement learning learning from just few trials, the learning system can no longer explore all learn-able solutions but has to prioritize one solution over others – independent of the observed data. Such prioritization requires explicit or implicit assumptions, often called ‘induction biases’ in machine learning. Extrapolation to new robot learning tasks requires induction biases deeply rooted in general principles and domain knowledge from robotics, physics and control. Empirical evaluations on a several robot systems illustrate the effectiveness and applicability to learning control on an anthropomorphic robot arm. These robot motor skills range from toy examples (e.g., paddling a ball, ball-in-a-cup) to playing robot table tennis, juggling and manipulation of various objects.
Jan Peters is a full professor (W3) for Intelligent Autonomous Systems at the Computer Science Department of the Technische Universitaet Darmstadt since 2011, and, at the same time, he is the dept head of the research department on Systems AI for Robot Learning (SAIROL) at the German Research Center for Artificial Intelligence (Deutsches Forschungszentrum für Künstliche Intelligenz, DFKI) since 2022. He is also is a founding research faculty member of the Hessian Center for Artificial Intelligence. Jan Peters has received the Dick Volz Best 2007 US PhD Thesis Runner-Up Award, the Robotics: Science & Systems – Early Career Spotlight, the |
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